The integration of solar energy in the energy supply reduces the cost of generating power from other resources but introduces its own challenges and costs. The challenges are mainly due to the variation of solar energy. The main factor impacting in the variable solar energy is the sky condition. In order to predict the output of the solar energy based system, it is, therefore, necessary to understand the sky conditions within temporal range.
Clouds are one of the key elements in the sky which cause the variation in the solar energy. The direct and non-direct solar irradiance largely depends on the cloud coverage. For example, when the sun is significantly covered by clouds, the solar radiance falling directly in the power grid decreases whereas when the sun is clear, there is a near constant energy received at the power location.
To predict future cloud coverage, the future locations of both clouds and the sun must be determined. The latter is readily available, for example, from sources such as an astronomical almanac. Similarly, the current state of the sky with respect to clouds is observable, for example, using visual imaging. Thus, it is desired to combine the available information on future sun position and the current state of the sky to provide an accurate prediction of direct and indirect solar irradiance.